Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations205
Missing cells59
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory132.3 KiB
Average record size in memory660.7 B

Variable types

Numeric16
Categorical10

Alerts

aspiration is highly overall correlated with compressionratio and 1 other fieldsHigh correlation
bodystyle is highly overall correlated with numofdoorsHigh correlation
bore is highly overall correlated with citympg and 9 other fieldsHigh correlation
citympg is highly overall correlated with bore and 7 other fieldsHigh correlation
compressionratio is highly overall correlated with aspiration and 3 other fieldsHigh correlation
curbweight is highly overall correlated with bore and 8 other fieldsHigh correlation
drivewheels is highly overall correlated with makeHigh correlation
enginelocation is highly overall correlated with enginesize and 5 other fieldsHigh correlation
enginesize is highly overall correlated with bore and 12 other fieldsHigh correlation
enginetype is highly overall correlated with enginesize and 3 other fieldsHigh correlation
fuelsystem is highly overall correlated with aspiration and 3 other fieldsHigh correlation
fueltype is highly overall correlated with compressionratio and 2 other fieldsHigh correlation
height is highly overall correlated with length and 3 other fieldsHigh correlation
highwaympg is highly overall correlated with bore and 9 other fieldsHigh correlation
horsepower is highly overall correlated with bore and 11 other fieldsHigh correlation
length is highly overall correlated with bore and 9 other fieldsHigh correlation
make is highly overall correlated with bore and 9 other fieldsHigh correlation
normalizedlosses is highly overall correlated with enginelocation and 1 other fieldsHigh correlation
numofcylinders is highly overall correlated with compressionratio and 6 other fieldsHigh correlation
numofdoors is highly overall correlated with bodystyle and 2 other fieldsHigh correlation
peakrpm is highly overall correlated with fueltypeHigh correlation
price is highly overall correlated with bore and 8 other fieldsHigh correlation
stroke is highly overall correlated with enginelocation and 1 other fieldsHigh correlation
symboling is highly overall correlated with height and 3 other fieldsHigh correlation
wheelbase is highly overall correlated with bore and 11 other fieldsHigh correlation
width is highly overall correlated with bore and 10 other fieldsHigh correlation
fueltype is highly imbalanced (53.9%)Imbalance
enginelocation is highly imbalanced (89.0%)Imbalance
numofcylinders is highly imbalanced (57.6%)Imbalance
normalizedlosses has 41 (20.0%) missing valuesMissing
bore has 4 (2.0%) missing valuesMissing
stroke has 4 (2.0%) missing valuesMissing
price has 4 (2.0%) missing valuesMissing
symboling has 67 (32.7%) zerosZeros

Reproduction

Analysis started2025-10-10 00:20:56.645203
Analysis finished2025-10-10 00:21:34.930387
Duration38.29 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2025-10-10T00:21:35.030979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2025-10-10T00:21:35.133929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
067
32.7%
154
26.3%
232
15.6%
327
13.2%
-122
 
10.7%
-23
 
1.5%
ValueCountFrequency (%)
-23
 
1.5%
-122
 
10.7%
067
32.7%
154
26.3%
232
15.6%
327
13.2%
ValueCountFrequency (%)
327
13.2%
232
15.6%
154
26.3%
067
32.7%
-122
 
10.7%
-23
 
1.5%

normalizedlosses
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)31.1%
Missing41
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean122
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:35.272345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile74
Q194
median115
Q3150
95-th percentile188
Maximum256
Range191
Interquartile range (IQR)56

Descriptive statistics

Standard deviation35.442168
Coefficient of variation (CV)0.29050957
Kurtosis0.52544039
Mean122
Median Absolute Deviation (MAD)24
Skewness0.76597642
Sum20008
Variance1256.1472
MonotonicityNot monotonic
2025-10-10T00:21:35.441587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16111
 
5.4%
918
 
3.9%
1507
 
3.4%
1286
 
2.9%
1046
 
2.9%
1346
 
2.9%
745
 
2.4%
1035
 
2.4%
1685
 
2.4%
955
 
2.4%
Other values (41)100
48.8%
(Missing)41
20.0%
ValueCountFrequency (%)
655
2.4%
745
2.4%
771
 
0.5%
781
 
0.5%
812
 
1.0%
833
1.5%
855
2.4%
872
 
1.0%
892
 
1.0%
901
 
0.5%
ValueCountFrequency (%)
2561
 
0.5%
2311
 
0.5%
1972
 
1.0%
1942
 
1.0%
1922
 
1.0%
1882
 
1.0%
1861
 
0.5%
1685
2.4%
1642
 
1.0%
16111
5.4%

make
Categorical

High correlation 

Distinct22
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
112 

Length

Max length13
Median length11
Mean length6.4780488
Min length3

Characters and Unicode

Total characters1328
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota32
15.6%
nissan18
 
8.8%
mazda17
 
8.3%
mitsubishi13
 
6.3%
honda13
 
6.3%
subaru12
 
5.9%
volkswagen12
 
5.9%
volvo11
 
5.4%
peugot11
 
5.4%
dodge9
 
4.4%
Other values (12)57
27.8%

Length

2025-10-10T00:21:35.595379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota32
15.6%
nissan18
 
8.8%
mazda17
 
8.3%
mitsubishi13
 
6.3%
honda13
 
6.3%
subaru12
 
5.9%
volkswagen12
 
5.9%
volvo11
 
5.4%
peugot11
 
5.4%
dodge9
 
4.4%
Other values (12)57
27.8%

Most occurring characters

ValueCountFrequency (%)
a154
 
11.6%
o152
 
11.4%
s109
 
8.2%
t100
 
7.5%
e81
 
6.1%
u76
 
5.7%
n71
 
5.3%
i68
 
5.1%
d63
 
4.7%
m57
 
4.3%
Other values (15)397
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a154
 
11.6%
o152
 
11.4%
s109
 
8.2%
t100
 
7.5%
e81
 
6.1%
u76
 
5.7%
n71
 
5.3%
i68
 
5.1%
d63
 
4.7%
m57
 
4.3%
Other values (15)397
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a154
 
11.6%
o152
 
11.4%
s109
 
8.2%
t100
 
7.5%
e81
 
6.1%
u76
 
5.7%
n71
 
5.3%
i68
 
5.1%
d63
 
4.7%
m57
 
4.3%
Other values (15)397
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a154
 
11.6%
o152
 
11.4%
s109
 
8.2%
t100
 
7.5%
e81
 
6.1%
u76
 
5.7%
n71
 
5.3%
i68
 
5.1%
d63
 
4.7%
m57
 
4.3%
Other values (15)397
29.9%

fueltype
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas185
90.2%
diesel20
 
9.8%

Length

2025-10-10T00:21:35.735244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:35.825286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gas185
90.2%
diesel20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s205
30.4%
g185
27.4%
a185
27.4%
e40
 
5.9%
d20
 
3.0%
i20
 
3.0%
l20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s205
30.4%
g185
27.4%
a185
27.4%
e40
 
5.9%
d20
 
3.0%
i20
 
3.0%
l20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s205
30.4%
g185
27.4%
a185
27.4%
e40
 
5.9%
d20
 
3.0%
i20
 
3.0%
l20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s205
30.4%
g185
27.4%
a185
27.4%
e40
 
5.9%
d20
 
3.0%
i20
 
3.0%
l20
 
3.0%

aspiration
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
std
168 
turbo
37 

Length

Max length5
Median length3
Mean length3.3609756
Min length3

Characters and Unicode

Total characters689
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std168
82.0%
turbo37
 
18.0%

Length

2025-10-10T00:21:35.963804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:36.048376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
std168
82.0%
turbo37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t205
29.8%
s168
24.4%
d168
24.4%
u37
 
5.4%
r37
 
5.4%
b37
 
5.4%
o37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t205
29.8%
s168
24.4%
d168
24.4%
u37
 
5.4%
r37
 
5.4%
b37
 
5.4%
o37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t205
29.8%
s168
24.4%
d168
24.4%
u37
 
5.4%
r37
 
5.4%
b37
 
5.4%
o37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t205
29.8%
s168
24.4%
d168
24.4%
u37
 
5.4%
r37
 
5.4%
b37
 
5.4%
o37
 
5.4%

numofdoors
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing2
Missing (%)1.0%
Memory size10.7 KiB
four
114 
two
89 

Length

Max length4
Median length4
Mean length3.5615764
Min length3

Characters and Unicode

Total characters723
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four114
55.6%
two89
43.4%
(Missing)2
 
1.0%

Length

2025-10-10T00:21:36.143736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:36.221121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
four114
56.2%
two89
43.8%

Most occurring characters

ValueCountFrequency (%)
o203
28.1%
f114
15.8%
u114
15.8%
r114
15.8%
t89
12.3%
w89
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o203
28.1%
f114
15.8%
u114
15.8%
r114
15.8%
t89
12.3%
w89
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o203
28.1%
f114
15.8%
u114
15.8%
r114
15.8%
t89
12.3%
w89
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o203
28.1%
f114
15.8%
u114
15.8%
r114
15.8%
t89
12.3%
w89
12.3%

bodystyle
Categorical

High correlation 

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6195122
Min length5

Characters and Unicode

Total characters1357
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan96
46.8%
hatchback70
34.1%
wagon25
 
12.2%
hardtop8
 
3.9%
convertible6
 
2.9%

Length

2025-10-10T00:21:36.314243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:36.408967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan96
46.8%
hatchback70
34.1%
wagon25
 
12.2%
hardtop8
 
3.9%
convertible6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a269
19.8%
h148
10.9%
c146
10.8%
n127
9.4%
e108
8.0%
d104
 
7.7%
s96
 
7.1%
t84
 
6.2%
b76
 
5.6%
k70
 
5.2%
Other values (8)129
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a269
19.8%
h148
10.9%
c146
10.8%
n127
9.4%
e108
8.0%
d104
 
7.7%
s96
 
7.1%
t84
 
6.2%
b76
 
5.6%
k70
 
5.2%
Other values (8)129
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a269
19.8%
h148
10.9%
c146
10.8%
n127
9.4%
e108
8.0%
d104
 
7.7%
s96
 
7.1%
t84
 
6.2%
b76
 
5.6%
k70
 
5.2%
Other values (8)129
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a269
19.8%
h148
10.9%
c146
10.8%
n127
9.4%
e108
8.0%
d104
 
7.7%
s96
 
7.1%
t84
 
6.2%
b76
 
5.6%
k70
 
5.2%
Other values (8)129
9.5%

drivewheels
Categorical

High correlation 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
fwd
120 
rwd
76 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd120
58.5%
rwd76
37.1%
4wd9
 
4.4%

Length

2025-10-10T00:21:36.531753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:36.607337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd120
58.5%
rwd76
37.1%
4wd9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w205
33.3%
d205
33.3%
f120
19.5%
r76
 
12.4%
49
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w205
33.3%
d205
33.3%
f120
19.5%
r76
 
12.4%
49
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w205
33.3%
d205
33.3%
f120
19.5%
r76
 
12.4%
49
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w205
33.3%
d205
33.3%
f120
19.5%
r76
 
12.4%
49
 
1.5%

enginelocation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
front
202 
rear
 
3

Length

Max length5
Median length5
Mean length4.9853659
Min length4

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front202
98.5%
rear3
 
1.5%

Length

2025-10-10T00:21:36.718785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:36.791389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
front202
98.5%
rear3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r208
20.4%
f202
19.8%
o202
19.8%
n202
19.8%
t202
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r208
20.4%
f202
19.8%
o202
19.8%
n202
19.8%
t202
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r208
20.4%
f202
19.8%
o202
19.8%
n202
19.8%
t202
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r208
20.4%
f202
19.8%
o202
19.8%
n202
19.8%
t202
19.8%
e3
 
0.3%
a3
 
0.3%

wheelbase
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:36.910562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2025-10-10T00:21:37.101373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.521
 
10.2%
93.720
 
9.8%
95.713
 
6.3%
96.58
 
3.9%
98.47
 
3.4%
97.37
 
3.4%
96.36
 
2.9%
107.96
 
2.9%
99.16
 
2.9%
98.86
 
2.9%
Other values (43)105
51.2%
ValueCountFrequency (%)
86.62
 
1.0%
88.41
 
0.5%
88.62
 
1.0%
89.53
 
1.5%
91.32
 
1.0%
931
 
0.5%
93.15
 
2.4%
93.31
 
0.5%
93.720
9.8%
94.31
 
0.5%
ValueCountFrequency (%)
120.91
 
0.5%
115.62
 
1.0%
114.24
2.0%
1132
 
1.0%
1121
 
0.5%
1103
1.5%
109.15
2.4%
1081
 
0.5%
107.96
2.9%
106.71
 
0.5%

length
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:37.252122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2025-10-10T00:21:37.406403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.315
 
7.3%
188.811
 
5.4%
186.77
 
3.4%
166.37
 
3.4%
171.77
 
3.4%
177.86
 
2.9%
165.36
 
2.9%
176.26
 
2.9%
186.66
 
2.9%
1725
 
2.4%
Other values (65)129
62.9%
ValueCountFrequency (%)
141.11
 
0.5%
144.62
 
1.0%
1503
 
1.5%
155.93
 
1.5%
156.91
 
0.5%
157.11
 
0.5%
157.315
7.3%
157.91
 
0.5%
158.73
 
1.5%
158.81
 
0.5%
ValueCountFrequency (%)
208.11
 
0.5%
202.62
1.0%
199.62
1.0%
199.21
 
0.5%
198.94
2.0%
1971
 
0.5%
193.81
 
0.5%
192.73
1.5%
191.71
 
0.5%
190.92
1.0%

width
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:37.610258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2025-10-10T00:21:37.757862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.824
 
11.7%
66.523
 
11.2%
65.415
 
7.3%
63.611
 
5.4%
64.410
 
4.9%
68.410
 
4.9%
649
 
4.4%
65.58
 
3.9%
65.27
 
3.4%
64.26
 
2.9%
Other values (34)82
40.0%
ValueCountFrequency (%)
60.31
 
0.5%
61.81
 
0.5%
62.51
 
0.5%
63.41
 
0.5%
63.611
5.4%
63.824
11.7%
63.93
 
1.5%
649
 
4.4%
64.12
 
1.0%
64.26
 
2.9%
ValueCountFrequency (%)
72.31
 
0.5%
721
 
0.5%
71.73
1.5%
71.43
1.5%
70.91
 
0.5%
70.61
 
0.5%
70.51
 
0.5%
70.33
1.5%
69.62
1.0%
68.94
2.0%

height
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:37.922035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2025-10-10T00:21:38.114007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.814
 
6.8%
5212
 
5.9%
55.712
 
5.9%
54.510
 
4.9%
54.110
 
4.9%
55.59
 
4.4%
56.78
 
3.9%
54.38
 
3.9%
52.67
 
3.4%
51.67
 
3.4%
Other values (39)108
52.7%
ValueCountFrequency (%)
47.81
 
0.5%
48.82
 
1.0%
49.42
 
1.0%
49.64
 
2.0%
49.73
 
1.5%
50.26
2.9%
50.52
 
1.0%
50.65
 
2.4%
50.814
6.8%
511
 
0.5%
ValueCountFrequency (%)
59.82
 
1.0%
59.13
 
1.5%
58.74
2.0%
58.31
 
0.5%
57.53
 
1.5%
56.78
3.9%
56.52
 
1.0%
56.32
 
1.0%
56.23
 
1.5%
56.17
3.4%

curbweight
Real number (ℝ)

High correlation 

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:38.275286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2025-10-10T00:21:38.447936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23854
 
2.0%
19183
 
1.5%
19893
 
1.5%
22753
 
1.5%
21912
 
1.0%
21282
 
1.0%
19672
 
1.0%
19092
 
1.0%
18762
 
1.0%
23372
 
1.0%
Other values (161)180
87.8%
ValueCountFrequency (%)
14881
0.5%
17131
0.5%
18191
0.5%
18371
0.5%
18742
1.0%
18762
1.0%
18891
0.5%
18901
0.5%
19001
0.5%
19051
0.5%
ValueCountFrequency (%)
40662
1.0%
39501
0.5%
39001
0.5%
37701
0.5%
37501
0.5%
37401
0.5%
37151
0.5%
36851
0.5%
35151
0.5%
35051
0.5%

enginetype
Categorical

High correlation 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc148
72.2%
ohcf15
 
7.3%
ohcv13
 
6.3%
dohc12
 
5.9%
l12
 
5.9%
rotor4
 
2.0%
dohcv1
 
0.5%

Length

2025-10-10T00:21:38.597164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:38.707019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ohc148
72.2%
ohcf15
 
7.3%
ohcv13
 
6.3%
dohc12
 
5.9%
l12
 
5.9%
rotor4
 
2.0%
dohcv1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o197
30.7%
h189
29.5%
c189
29.5%
f15
 
2.3%
v14
 
2.2%
d13
 
2.0%
l12
 
1.9%
r8
 
1.2%
t4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o197
30.7%
h189
29.5%
c189
29.5%
f15
 
2.3%
v14
 
2.2%
d13
 
2.0%
l12
 
1.9%
r8
 
1.2%
t4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o197
30.7%
h189
29.5%
c189
29.5%
f15
 
2.3%
v14
 
2.2%
d13
 
2.0%
l12
 
1.9%
r8
 
1.2%
t4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o197
30.7%
h189
29.5%
c189
29.5%
f15
 
2.3%
v14
 
2.2%
d13
 
2.0%
l12
 
1.9%
r8
 
1.2%
t4
 
0.6%

numofcylinders
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four159
77.6%
six24
 
11.7%
five11
 
5.4%
eight5
 
2.4%
two4
 
2.0%
twelve1
 
0.5%
three1
 
0.5%

Length

2025-10-10T00:21:38.850249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:38.971888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
four159
77.6%
six24
 
11.7%
five11
 
5.4%
eight5
 
2.4%
two4
 
2.0%
twelve1
 
0.5%
three1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f170
21.2%
o163
20.4%
r160
20.0%
u159
19.9%
i40
 
5.0%
s24
 
3.0%
x24
 
3.0%
e20
 
2.5%
v12
 
1.5%
t11
 
1.4%
Other values (4)17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f170
21.2%
o163
20.4%
r160
20.0%
u159
19.9%
i40
 
5.0%
s24
 
3.0%
x24
 
3.0%
e20
 
2.5%
v12
 
1.5%
t11
 
1.4%
Other values (4)17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f170
21.2%
o163
20.4%
r160
20.0%
u159
19.9%
i40
 
5.0%
s24
 
3.0%
x24
 
3.0%
e20
 
2.5%
v12
 
1.5%
t11
 
1.4%
Other values (4)17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f170
21.2%
o163
20.4%
r160
20.0%
u159
19.9%
i40
 
5.0%
s24
 
3.0%
x24
 
3.0%
e20
 
2.5%
v12
 
1.5%
t11
 
1.4%
Other values (4)17
 
2.1%

enginesize
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:39.143850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2025-10-10T00:21:39.300800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
12215
 
7.3%
9215
 
7.3%
9714
 
6.8%
9814
 
6.8%
10813
 
6.3%
11012
 
5.9%
9012
 
5.9%
1098
 
3.9%
1207
 
3.4%
1417
 
3.4%
Other values (34)88
42.9%
ValueCountFrequency (%)
611
 
0.5%
703
 
1.5%
791
 
0.5%
801
 
0.5%
9012
5.9%
915
 
2.4%
9215
7.3%
9714
6.8%
9814
6.8%
1031
 
0.5%
ValueCountFrequency (%)
3261
 
0.5%
3081
 
0.5%
3041
 
0.5%
2582
 
1.0%
2342
 
1.0%
2093
1.5%
2031
 
0.5%
1943
1.5%
1834
2.0%
1816
2.9%

fuelsystem
Categorical

High correlation 

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi94
45.9%
2bbl66
32.2%
idi20
 
9.8%
1bbl11
 
5.4%
spdi9
 
4.4%
4bbl3
 
1.5%
mfi1
 
0.5%
spfi1
 
0.5%

Length

2025-10-10T00:21:39.428796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T00:21:39.530800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mpfi94
45.9%
2bbl66
32.2%
idi20
 
9.8%
1bbl11
 
5.4%
spdi9
 
4.4%
4bbl3
 
1.5%
mfi1
 
0.5%
spfi1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b160
20.0%
i145
18.1%
p104
13.0%
f96
12.0%
m95
11.9%
l80
10.0%
266
8.3%
d29
 
3.6%
111
 
1.4%
s10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b160
20.0%
i145
18.1%
p104
13.0%
f96
12.0%
m95
11.9%
l80
10.0%
266
8.3%
d29
 
3.6%
111
 
1.4%
s10
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b160
20.0%
i145
18.1%
p104
13.0%
f96
12.0%
m95
11.9%
l80
10.0%
266
8.3%
d29
 
3.6%
111
 
1.4%
s10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b160
20.0%
i145
18.1%
p104
13.0%
f96
12.0%
m95
11.9%
l80
10.0%
266
8.3%
d29
 
3.6%
111
 
1.4%
s10
 
1.3%

bore
Real number (ℝ)

High correlation  Missing 

Distinct38
Distinct (%)18.9%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.3297512
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:39.666738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.59
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.27353873
Coefficient of variation (CV)0.0821499
Kurtosis-0.8289454
Mean3.3297512
Median Absolute Deviation (MAD)0.26
Skewness0.02001551
Sum669.28
Variance0.074823438
MonotonicityNot monotonic
2025-10-10T00:21:39.809055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.6223
 
11.2%
3.1920
 
9.8%
3.1515
 
7.3%
3.0312
 
5.9%
2.9712
 
5.9%
3.469
 
4.4%
3.318
 
3.9%
3.438
 
3.9%
3.788
 
3.9%
3.277
 
3.4%
Other values (28)79
38.5%
ValueCountFrequency (%)
2.541
 
0.5%
2.681
 
0.5%
2.917
3.4%
2.921
 
0.5%
2.9712
5.9%
2.991
 
0.5%
3.015
2.4%
3.0312
5.9%
3.056
2.9%
3.081
 
0.5%
ValueCountFrequency (%)
3.942
 
1.0%
3.82
 
1.0%
3.788
 
3.9%
3.761
 
0.5%
3.743
 
1.5%
3.75
 
2.4%
3.632
 
1.0%
3.6223
11.2%
3.611
 
0.5%
3.61
 
0.5%

stroke
Real number (ℝ)

High correlation  Missing 

Distinct36
Distinct (%)17.9%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.2554229
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:39.937003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31671745
Coefficient of variation (CV)0.097289189
Kurtosis2.0742435
Mean3.2554229
Median Absolute Deviation (MAD)0.17
Skewness-0.68312219
Sum654.34
Variance0.10030995
MonotonicityNot monotonic
2025-10-10T00:21:40.067851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.420
 
9.8%
3.2314
 
6.8%
3.1514
 
6.8%
3.0314
 
6.8%
3.3913
 
6.3%
2.6411
 
5.4%
3.359
 
4.4%
3.299
 
4.4%
3.468
 
3.9%
3.586
 
2.9%
Other values (26)83
40.5%
ValueCountFrequency (%)
2.071
 
0.5%
2.192
 
1.0%
2.361
 
0.5%
2.6411
5.4%
2.682
 
1.0%
2.761
 
0.5%
2.82
 
1.0%
2.871
 
0.5%
2.93
 
1.5%
3.0314
6.8%
ValueCountFrequency (%)
4.172
 
1.0%
3.93
 
1.5%
3.864
2.0%
3.645
2.4%
3.586
2.9%
3.544
2.0%
3.525
2.4%
3.56
2.9%
3.474
2.0%
3.468
3.9%

compressionratio
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:40.225775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2025-10-10T00:21:40.443567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
946
22.4%
9.426
12.7%
8.514
 
6.8%
9.513
 
6.3%
9.311
 
5.4%
8.79
 
4.4%
9.28
 
3.9%
88
 
3.9%
77
 
3.4%
235
 
2.4%
Other values (22)58
28.3%
ValueCountFrequency (%)
77
3.4%
7.55
 
2.4%
7.64
 
2.0%
7.72
 
1.0%
7.81
 
0.5%
88
3.9%
8.12
 
1.0%
8.33
 
1.5%
8.45
 
2.4%
8.514
6.8%
ValueCountFrequency (%)
235
2.4%
22.71
 
0.5%
22.53
1.5%
221
 
0.5%
21.91
 
0.5%
21.54
2.0%
215
2.4%
11.51
 
0.5%
10.11
 
0.5%
103
1.5%

horsepower
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)29.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean104.25616
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:40.670814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile181.4
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.714369
Coefficient of variation (CV)0.38093068
Kurtosis2.6232798
Mean104.25616
Median Absolute Deviation (MAD)25
Skewness1.3910295
Sum21164
Variance1577.2311
MonotonicityNot monotonic
2025-10-10T00:21:40.899853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6819
 
9.3%
7011
 
5.4%
6910
 
4.9%
1169
 
4.4%
1108
 
3.9%
957
 
3.4%
886
 
2.9%
1146
 
2.9%
626
 
2.9%
1016
 
2.9%
Other values (49)115
56.1%
ValueCountFrequency (%)
481
 
0.5%
522
 
1.0%
551
 
0.5%
562
 
1.0%
581
 
0.5%
601
 
0.5%
626
 
2.9%
641
 
0.5%
6819
9.3%
6910
4.9%
ValueCountFrequency (%)
2881
 
0.5%
2621
 
0.5%
2073
1.5%
2001
 
0.5%
1842
1.0%
1823
1.5%
1762
1.0%
1751
 
0.5%
1622
1.0%
1612
1.0%

peakrpm
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)11.3%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean5125.3695
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:41.068011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5990
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation479.33456
Coefficient of variation (CV)0.093521953
Kurtosis0.056526492
Mean5125.3695
Median Absolute Deviation (MAD)300
Skewness0.073236691
Sum1040450
Variance229761.62
MonotonicityNot monotonic
2025-10-10T00:21:41.646377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
550037
18.0%
480036
17.6%
500027
13.2%
520023
11.2%
540013
 
6.3%
60009
 
4.4%
45007
 
3.4%
58007
 
3.4%
52507
 
3.4%
42005
 
2.4%
Other values (13)32
15.6%
ValueCountFrequency (%)
41505
 
2.4%
42005
 
2.4%
42503
 
1.5%
43504
 
2.0%
44003
 
1.5%
45007
 
3.4%
46501
 
0.5%
47504
 
2.0%
480036
17.6%
49001
 
0.5%
ValueCountFrequency (%)
66002
 
1.0%
60009
 
4.4%
59003
 
1.5%
58007
 
3.4%
57501
 
0.5%
56001
 
0.5%
550037
18.0%
540013
 
6.3%
53001
 
0.5%
52507
 
3.4%

citympg
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:42.124806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2025-10-10T00:21:42.573248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3128
13.7%
1927
13.2%
2422
10.7%
2714
 
6.8%
1713
 
6.3%
2612
 
5.9%
2312
 
5.9%
218
 
3.9%
258
 
3.9%
308
 
3.9%
Other values (19)53
25.9%
ValueCountFrequency (%)
131
 
0.5%
142
 
1.0%
153
 
1.5%
166
 
2.9%
1713
6.3%
183
 
1.5%
1927
13.2%
203
 
1.5%
218
 
3.9%
224
 
2.0%
ValueCountFrequency (%)
491
 
0.5%
471
 
0.5%
451
 
0.5%
387
3.4%
376
2.9%
361
 
0.5%
351
 
0.5%
341
 
0.5%
331
 
0.5%
321
 
0.5%

highwaympg
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:43.222975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2025-10-10T00:21:43.915337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2519
 
9.3%
2417
 
8.3%
3817
 
8.3%
3016
 
7.8%
3216
 
7.8%
3414
 
6.8%
2813
 
6.3%
3713
 
6.3%
2910
 
4.9%
339
 
4.4%
Other values (20)61
29.8%
ValueCountFrequency (%)
162
 
1.0%
171
 
0.5%
182
 
1.0%
192
 
1.0%
202
 
1.0%
228
3.9%
237
 
3.4%
2417
8.3%
2519
9.3%
263
 
1.5%
ValueCountFrequency (%)
541
 
0.5%
531
 
0.5%
501
 
0.5%
472
 
1.0%
462
 
1.0%
434
 
2.0%
423
 
1.5%
413
 
1.5%
392
 
1.0%
3817
8.3%

price
Real number (ℝ)

High correlation  Missing 

Distinct186
Distinct (%)92.5%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean13207.129
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-10-10T00:21:44.380044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6189
Q17775
median10295
Q316500
95-th percentile32528
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation7947.0663
Coefficient of variation (CV)0.60172549
Kurtosis3.2315369
Mean13207.129
Median Absolute Deviation (MAD)3306
Skewness1.8096753
Sum2654633
Variance63155863
MonotonicityNot monotonic
2025-10-10T00:21:45.327717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165002
 
1.0%
62292
 
1.0%
76092
 
1.0%
79572
 
1.0%
66922
 
1.0%
55722
 
1.0%
84952
 
1.0%
88452
 
1.0%
72952
 
1.0%
89212
 
1.0%
Other values (176)181
88.3%
(Missing)4
 
2.0%
ValueCountFrequency (%)
51181
0.5%
51511
0.5%
51951
0.5%
53481
0.5%
53891
0.5%
53991
0.5%
54991
0.5%
55722
1.0%
60951
0.5%
61891
0.5%
ValueCountFrequency (%)
454001
0.5%
413151
0.5%
409601
0.5%
370281
0.5%
368801
0.5%
360001
0.5%
355501
0.5%
350561
0.5%
341841
0.5%
340281
0.5%

Interactions

2025-10-10T00:21:31.656287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:20:59.337988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:01.659483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:03.368764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.482930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:08.222599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:10.172819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:11.943624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:14.260872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.720009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:18.528255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.581716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:22.821604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:24.438099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:26.237907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:29.707298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:31.782243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:20:59.461341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:01.763790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:03.475959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.596988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:08.323540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:10.272429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:12.087265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:14.387800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.836854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:18.624102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.686759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:22.914816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:24.540729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:26.408613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:29.875044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:31.902950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:20:59.604080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:01.864387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:03.582331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.702975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:08.440744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:10.371648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:12.232195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:14.537634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.970055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:18.719267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.799920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:23.028323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:24.646814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:26.584022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:30.045675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:32.023697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:20:59.748932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:01.967450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:03.681754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.812750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:08.547804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:10.484329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:12.371085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:14.683112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:17.080781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:18.814139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.904835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:23.133995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:24.748425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:26.745448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:30.209344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:32.740335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:20:59.907364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:02.082123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:03.794611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.926040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:08.650189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:10.589886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:12.519663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:14.858511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-10T00:21:32.999688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-10T00:21:02.315124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:05.395663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:07.136997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-10T00:21:17.530568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:19.235523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:21.365154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:23.535919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:25.145481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:27.388716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:30.636649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:33.225419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:00.438349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:02.513906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:05.600211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-10T00:21:10.973909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-10T00:21:15.921633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:17.632761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:19.472851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:21.468690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:23.624441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-10T00:21:33.351888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:00.593971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:02.620638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-10T00:21:07.491386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:09.149716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:11.074968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:13.227286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.023607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:17.743050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:19.692659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:21.581251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:23.727877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:25.356250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:27.716300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:30.862092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:33.462514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:00.720678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:02.718915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:05.812260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:07.589065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:09.560158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:11.171980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:13.364993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.119256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:17.839972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:19.958179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:21.680289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:23.821453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:25.449592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:27.858409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:30.961337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:33.599195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:00.877335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:02.823475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:05.922750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:07.694221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:09.663181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:11.275291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:13.514612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.216467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:17.971755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.072941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:21.781809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:23.922835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:25.552119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:28.024224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:31.071758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:33.710061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:01.030359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:02.933258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.022830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:07.800189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:09.760794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:11.369724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:13.648580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.303844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:18.081805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.163160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:21.886340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:24.018028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:25.645622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:28.167265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:31.171181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:33.831180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:01.182170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:03.032494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.116767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:07.893074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:09.852291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:11.461721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:13.795720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.394741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:18.177721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.251472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:21.992825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:24.109517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:25.769943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:28.323256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:31.279886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:33.959510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:01.365078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:03.154381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.240811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-10T00:21:09.960670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:11.590952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:13.943079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.503806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:18.284171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.363721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:22.105093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:24.216019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:25.920750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:28.458495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:31.393408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:34.089002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:01.545617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:03.264882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:06.366259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:08.111111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:10.065643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:11.772365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:14.108299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:16.616103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:18.407769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:20.476865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:22.703571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:24.333855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:26.078030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:29.352908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T00:21:31.536598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-10T00:21:47.064399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
aspirationbodystyleborecitympgcompressionratiocurbweightdrivewheelsenginelocationenginesizeenginetypefuelsystemfueltypeheighthighwaympghorsepowerlengthmakenormalizedlossesnumofcylindersnumofdoorspeakrpmpricestrokesymbolingwheelbasewidth
aspiration1.0000.0000.3280.1860.5540.3750.1180.0000.2710.1500.6100.3740.2370.3190.3420.2070.4100.0000.1960.0000.3140.3940.2590.1850.3100.301
bodystyle0.0001.0000.1510.0000.0480.2300.2140.4380.2020.1320.1440.1730.4970.0000.1850.2410.3170.2050.0680.7480.0640.2400.1520.3340.3340.128
bore0.3280.1511.000-0.620-0.1650.7000.4390.3250.7250.4350.3560.1700.226-0.6270.6470.6420.537-0.0640.2450.215-0.3100.649-0.082-0.1820.5410.611
citympg0.1860.000-0.6201.0000.479-0.8130.3800.110-0.7300.2090.3040.389-0.0690.968-0.913-0.6700.360-0.2530.4240.051-0.132-0.831-0.035-0.018-0.493-0.688
compressionratio0.5540.048-0.1650.4791.000-0.2190.1140.000-0.2350.3380.5180.9930.0000.445-0.355-0.1930.493-0.0510.5210.182-0.026-0.178-0.0680.023-0.126-0.146
curbweight0.3750.2300.700-0.813-0.2191.0000.4560.1000.8780.3270.2920.3050.346-0.8340.8070.8900.4940.0860.4820.267-0.2380.9140.163-0.2560.7650.864
drivewheels0.1180.2140.4390.3800.1140.4561.0000.1240.4690.4250.3870.0880.3600.4370.4010.4090.6030.3430.3360.0510.2450.4430.3340.2660.4170.403
enginelocation0.0000.4380.3250.1100.0000.1000.1241.0000.6190.3990.0000.0000.2720.1010.8430.0000.7031.0000.2880.0680.4470.4680.6140.2720.5680.160
enginesize0.2710.2020.725-0.730-0.2350.8780.4690.6191.0000.5270.3330.1570.200-0.7210.8200.7830.5330.0810.6420.202-0.2750.8280.293-0.1770.6480.771
enginetype0.1500.1320.4350.2090.3380.3270.4250.3990.5271.0000.3770.2500.3880.3250.5150.3170.6290.3490.5460.2010.3590.2610.4400.2220.3530.369
fuelsystem0.6100.1440.3560.3040.5180.2920.3870.0000.3330.3771.0000.9850.2920.3410.3200.3260.5100.1510.3730.2400.3640.2870.3280.2660.2260.246
fueltype0.3740.1730.1700.3890.9930.3050.0880.0000.1570.2500.9851.0000.2770.3360.2200.1100.3700.1630.1550.1490.5930.3400.3710.2170.3410.233
height0.2370.4970.226-0.0690.0000.3460.3600.2720.2000.3880.2920.2771.000-0.1330.0090.5250.480-0.3920.3500.536-0.3010.264-0.026-0.5230.6330.350
highwaympg0.3190.000-0.6270.9680.445-0.8340.4370.101-0.7210.3250.3410.336-0.1331.000-0.884-0.6980.404-0.1970.5000.131-0.056-0.827-0.0340.053-0.539-0.701
horsepower0.3420.1850.647-0.913-0.3550.8070.4010.8430.8200.5150.3200.2200.009-0.8841.0000.6630.4590.2390.5640.1780.1120.8510.138-0.0100.5030.692
length0.2070.2410.642-0.670-0.1930.8900.4090.0000.7830.3170.3260.1100.525-0.6980.6631.0000.5000.0210.3560.361-0.2720.8100.181-0.3960.9120.888
make0.4100.3170.5370.3600.4930.4940.6030.7030.5330.6290.5100.3700.4800.4040.4590.5001.0000.4400.5440.3000.4690.3700.5800.4430.5070.527
normalizedlosses0.0000.205-0.064-0.253-0.0510.0860.3431.0000.0810.3490.1510.163-0.392-0.1970.2390.0210.4401.0000.2760.4170.2980.1880.0930.527-0.1080.114
numofcylinders0.1960.0680.2450.4240.5210.4820.3360.2880.6420.5460.3730.1550.3500.5000.5640.3560.5440.2761.0000.1350.2830.4470.2560.1600.3160.567
numofdoors0.0000.7480.2150.0510.1820.2670.0510.0680.2020.2010.2400.1490.5360.1310.1780.3610.3000.4170.1351.0000.2450.0000.1490.6820.4440.297
peakrpm0.3140.064-0.310-0.132-0.026-0.2380.2450.447-0.2750.3590.3640.593-0.301-0.0560.112-0.2720.4690.2980.2830.2451.000-0.083-0.0710.285-0.315-0.201
price0.3940.2400.649-0.831-0.1780.9140.4430.4680.8280.2610.2870.3400.264-0.8270.8510.8100.3700.1880.4470.000-0.0831.0000.118-0.1430.6820.812
stroke0.2590.152-0.082-0.035-0.0680.1630.3340.6140.2930.4400.3280.371-0.026-0.0340.1380.1810.5800.0930.2560.149-0.0710.1181.000-0.0150.2240.241
symboling0.1850.334-0.182-0.0180.023-0.2560.2660.272-0.1770.2220.2660.217-0.5230.053-0.010-0.3960.4430.5270.1600.6820.285-0.143-0.0151.000-0.538-0.254
wheelbase0.3100.3340.541-0.493-0.1260.7650.4170.5680.6480.3530.2260.3410.633-0.5390.5030.9120.507-0.1080.3160.444-0.3150.6820.224-0.5381.0000.812
width0.3010.1280.611-0.688-0.1460.8640.4030.1600.7710.3690.2460.2330.350-0.7010.6920.8880.5270.1140.5670.297-0.2010.8120.241-0.2540.8121.000

Missing values

2025-10-10T00:21:34.329637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-10T00:21:34.566318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-10T00:21:34.778520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

symbolingnormalizedlossesmakefueltypeaspirationnumofdoorsbodystyledrivewheelsenginelocationwheelbaselengthwidthheightcurbweightenginetypenumofcylindersenginesizefuelsystemborestrokecompressionratiohorsepowerpeakrpmcitympghighwaympgprice
03NaNalfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212713495.0
13NaNalfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212716500.0
21NaNalfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.0154.05000.0192616500.0
32164.0audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.0102.05500.0243013950.0
42164.0audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.0115.05500.0182217450.0
52NaNaudigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.5110.05500.0192515250.0
61158.0audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.5110.05500.0192517710.0
71NaNaudigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.5110.05500.0192518920.0
81158.0audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.3140.05500.0172023875.0
90NaNaudigasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.0160.05500.01622NaN
symbolingnormalizedlossesmakefueltypeaspirationnumofdoorsbodystyledrivewheelsenginelocationwheelbaselengthwidthheightcurbweightenginetypenumofcylindersenginesizefuelsystemborestrokecompressionratiohorsepowerpeakrpmcitympghighwaympgprice
195-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.5114.05400.0232813415.0
196-2103.0volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.5114.05400.0242815985.0
197-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.5114.05400.0242816515.0
198-2103.0volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.5162.05100.0172218420.0
199-174.0volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.5162.05100.0172218950.0
200-195.0volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.5114.05400.0232816845.0
201-195.0volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.7160.05300.0192519045.0
202-195.0volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.8134.05500.0182321485.0
203-195.0volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.0106.04800.0262722470.0
204-195.0volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.5114.05400.0192522625.0